Extending MPAS-NoahMP Model System Capability Beyond Weather Timescales: Evaluation of Hydroclimate and Land-Atmosphere Interactions

Subseasonal-to-seasonal (S2S) forecasts are critically important for anticipating socioeconomic impacts but remain a significant scientific challenge. Reliable forecasts beyond a 2-week timeframe require both accurate initial conditions and realistic representations of coupled Earth system processes. In particular, the Earth's land surface provides a key source of predictability for S2S predictions through land-atmosphere interactions. Here, we present a 25-year global 60-km simulation using the Model for Prediction Across Scales (MPAS) coupled to the Noah-MP land surface model, forced with ERA-5 sea surface temperature and sea ice boundary conditions. This study evaluates the model's climatology, systematic biases, and ability to represent land-atmosphere interactions, all of which are critical for future S2S applications. MPAS-NoahMP captures well the annual surface temperature in the tropics and midlatitudes, while winter temperatures in North America and Asia are sensitive to sea ice thickness and snowpack thermal properties. The global precipitation pattern is also effectively reproduced by MPAS-NoahMP, with slight dry biases in the tropics of the Eastern Pacific, Amazon, Congo Basin, and Maritime Continent, and wet biases in the tropical Indian ocean. The model accurately simulates spatial patterns of land-atmosphere coupling indices and hotspots, including winter snow-temperature coupling and summer soil moisture-latent heat flux coupling. Furthermore, MPAS-NoahMP reasonably represents the major modes of tropical variability, including the El Nino-Southern Oscillation and the Madden-Julian Oscillation. This study is the first to establish a long-term MPAS-NoahMP reference data set for understanding model biases and assessing land-atmosphere interactions, providing a foundation for guiding future improvements to the coupled system for S2S applications.